James B. Grace. 2006. Structural Equation Modeling and Natural Systems. Cambridge University Press. [amazon]
Ken A. Bollen. 1989. Structural Equations with Latent Variables. Wiley Press.[amazon]
Rex B. Kline. 2010. Principles and Practice of Structural Equation Modeling. The Guilford Press. [amazon]
Bill Shipley. 2000. Cause and Correlation in Biology. Cambridge University Press. [amazon]
Rick H. Holyle, ed. 2012. Handbook of Structural Equation Modeling. The Guilford Press. [amazon]
sem for biology (for this class)
lavaan google group
semnet
R for Data Science Wickham and Gromelund. Essential reading.
Getting used to R, RStudio, and R Markdown 2017. Chester Ismay. The basics.
R Programming for Data Science. 2016. Roger D. Peng. Provides a more detailed intro to basic R programming.
Exploratory Data Analysis with R. 2016. Roger D. Peng. Uses the tidyverse and ggplot2 for data exploration. Great introduction to these packages and how they can be made to sing together.
Efficient R Programming. 2016. Colin Gillespe and Robin Lovelace
Statistical Inference for Data Science. 2018. Brian Caffo. A wonderful book that is a companion to his Coursera course, but is open, and full of gret concepts and R examples.
Regression Models for Data Science in R. 2018. Brian Caffo. A wonderful primer on regression models. Incredibly thorough.
Advanced R. 2014. Great walkthrough of the details and guts of R. From novices to R wizards, you will learn things you never thought possible (or the actual reasoning behind that hacky stuff you’ve been doing for years).
Principles of Econometrics with R 2016. Constantin Colonescu. Yes, it’s econometrics, but there’s a lot here that’s very generalizable to biological data analysis in R as well.
A Tour of Time Series Analysis with R
Fundamentals of Data Visualization. 2018. Claus Wilke. A wonderful online collection of best principles and practices for data viz.
Forecasting: Principles and Practice. 2018. Rob Hyndman and George Athanasaopoules. A great intro to timeseries and forecasting in R.